init

filtering_purify.py

@@ -105,12 +105,14 @@ if __name__ == '__main__':
     df_size = pd.DataFrame([{"min entity": mef, "max predicate": mpf, "freq": x} for x, (mef, mpf) in zip(data_size_full, candidates)])
     df_size = df_size.pivot(index="min entity", columns="max predicate", values="freq")
     df_size.index.name = "min entity / max predicate"
+    df_size.to_csv("data/stats.data_size.csv")
+    print(df_size.to_markdown())
     df_size_p = pd.DataFrame(
         [{"min entity": mef, "max predicate": mpf, "freq": len(x)} for x, (mef, mpf) in zip(p_dist_full, candidates)])
     df_size_p = df_size_p.pivot(index="max predicate", columns="min entity", values="freq")
-    df_size['predicate'] = df_size_p.loc[10]
-    df_size.to_csv("data/stats.data_size.csv")
-    print(df_size.to_markdown())
+    df_size_p = df_size_p.loc[10]
+    df_size_p.to_csv("data/stats.predicate_size.csv")
+    print(df_size_p.to_markdown())
 
     # plot predicate distribution
     df_p = pd.DataFrame([dict(enumerate(sorted(p.values(), reverse=True))) for p in p_dist_full]).T